Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely used to simulate whole-body hemodynamics from physiological parameters, solving the corresponding inverse problem of mapping waveforms back to plausible physiological parameters remains both promising and challenging. Motivated by advances in simulation-based inference (SBI), we cast this inverse problem as statistical inference. In contrast to alternative approaches, SBI provides \textit{posterior distributions} for the parameters of interest, providing a \textit{multi-dimensional} representation of uncertainty for \textit{individual} measurements. We showcase this ability by performing an in-silico uncertainty analysis of five biomarkers of clinical interest comparing several measurement modalities. Beyond the corroboration of known facts, such as the feasibility of estimating heart rate, our study highlights the potential of estimating new biomarkers from standard-of-care measurements. SBI reveals practically relevant findings that cannot be captured by standard sensitivity analyses, such as the existence of sub-populations for which parameter estimation exhibits distinct uncertainty regimes. Finally, we study the gap between in-vivo and in-silico with the MIMIC-III waveform database and critically discuss how cardiovascular simulations can inform real-world data analysis.

翻译：过去几十年来，血流动力学模拟器稳步发展，已成为研究心血管系统在计算机环境下不可或缺的工具。尽管这类工具已常规用于从生理参数模拟全身血流动力学，但将波形反向映射到合理生理参数的逆问题研究既充满前景也面临挑战。受基于模拟的推断（Simulation-based Inference, SBI）研究进展的启发，我们将该逆问题形式化为统计推断问题。与其它方法相比，SBI能够为待估参数提供\textit{后验分布}，从而为\textit{个体}测量结果提供\textit{多维}不确定性表征。我们通过对五种临床兴趣生物标志物进行计算机模拟不确定性分析，并比较多种测量模态，展示了这一能力。除了验证已知结论（如心率估算的可行性），本研究还揭示了从标准临床测量中估算新生物标志物的潜力。SBI揭示了标准敏感性分析无法捕捉的实践相关发现，例如存在子群体使参数估计呈现不同不确定性模式。最后，我们利用MIMIC-III波形数据库研究了体内与计算机模拟之间的差距，并批判性地讨论了心血管模拟如何为真实世界数据分析提供信息。